Application Fraud

As data breaches proliferate, fraudsters are increasingly using stolen or synthetic identities for application fraud, opening fraudulent credit cards, loans, and mortgages. These forms of application fraud represented 32% of an estimated $16.8B of financial damage from identity theft in 2017. DataVisor’s Unsupervised Machine Learning Engine analyzes the hidden connections between fraudulent applications to detect suspicious applications even if each application in isolation is not suspicious. This allows DataVisor to stop application fraud in real time, without training data or labels, stopping the fraudster at account approval.

Stop Sophisticated Attack Techniques

IP Obfuscation

Armies of Free Emails

Fraudsters use popular free email services to mass register realistic-looking accounts to use for their own attacks or to sell to other fraudsters.

Scripted Logins

Attackers use sophisticated scripts to carry out large scale attacks, appearing as though the sessions are from many distinct users.

Device Obfuscation

Fraudsters utilize mobile device flashing, virtual machines and scripts to appear as though the login events are coming from different devices.

Why UML is Needed to Stop Application Fraud

The wide availability of personally identifiable information allows fraudsters to apply for accounts using stolen or synthetic identities. Synthetic identity theft, where fraudsters create an entirely new fake identity, is almost a perfect crime as there is no consumer victim to complain about the fraud. Coupled with sophisticated mass registration techniques, these synthetic accounts appear legitimate and remain under the radar when reviewed in isolation. DataVisor’s Unsupervised Machine Learning Engine analyzes all accounts simultaneously, allowing it to detect the hidden connections between fraudulent accounts, even if each account is not suspicious in isolation.

Accuracy and Coverage

By detecting entire crime rings at once, UML is able to achieve unrivaled detection accuracy and coverage at the same time.

Detect Unknown Threats

UML uncovers the hidden connections between accounts without training data or labels, allowing it to detect changing and entirely new attack patterns.

The DataVisor Platform

Unsupervised Machine Learning Engine

Predict new, unknown threats without labels or training data by analyzing hundreds of millions of accounts and events simultaneously using the industry’s most advanced unsupervised learning technology.

What’s Happening with Application Fraud

Wells, wells, wells, what do we have here? Last week the news broke that Wells Fargo had “been hit with $185 million in civil penalties for secretly opening millions of unauthorized deposit and credit card accounts

Device fingerprinting, i.e., collecting information from a device for the purposes of identification, is one of the main techniques used by online services for mobile fraud detection. The goal is to recognize “bad” devices used